This paper discusses a method for improving accuracy of fuzzy-rule-based classifiers using particle swarm optimization (PSO). Two different fuzzy classifiers are considered and optimized. The first classifier is based on Mamdani fuzzy inference system (M_PSO fuzzy classifier). The second classifier is based on TakagiSugeno fuzzy inference system (TS_PSO fuzzy classifier). The parameters of the ...

In fuzzy control, there is a large amount of parameters involved in the system design. Due to their interdependency, these parameters are sometimes conflicting causing an unavoidable trade-off among performance indices. It is difficult to discern the best combination of fuzzy parameters with respect to a given range of some performance indices. In this case, a clustering technique represents a ...

Uncertain input parameters may result from "fuzziness", "randomness" or "fuzzy randomness". With the use of fuzzy set theory, uncertain input parameters may be described mathematically as fuzzy variables or fuzzy random variables and may be integrated into safety assessment analysis. With the aid of -discretization involving the multiple solution of special optimization problems, fuzzy input pa...

The parameters setting of fuzzy controller is realised by means of the rules, of the inference methods, of the membership functions layout and especially of the universe ranges. The optimisation of fuzzy controllers parameters may be more complicated process than for classical ones, because the fuzzy controller, as non-linear, has seemingly more degrees of freedom. However its exploitation is o...

In this paper, for nonlinear, time-varying and delayed system, PID parameters self-tuning fuzzy control was applied to a level process. The method of using VC++ programming to realize fuzzy control algorithm and obtain the discrete fuzzy control table is proposed, which simplified the computing process greatly. Furthermore, combing the inquiry of fuzzy control table and PID function module in P...

In practical signal detection scenarios, parameters of a random process are often uncertain. In this paper, we model such uncertainties as fuzzy parameters of a stationary random process. A fuzzy Neyman-Pearson hypothesis test concept which accepts any number of fuzzy parameters is presented. A suitable decision rule is developed by applying theory for ordering fuzzy numbers, and stated in term...

The COCOMO Model is well known as the currently predominate model for software cost estimation. It allows one to work from linguistic variables to estimate software project effort and schedule. This basis in linguistic variables encourages research of the COCOMO Model as a fuzzy system. As is known in fuzzy circles and is shown here, fuzzy arithmetic based on the popular fuzzy extension princip...

in statistical inference, the point estimation problem is very crucial and has a wide range of applications. when, we deal with some concepts such as random variables, the parameters of interest and estimates may be reported/observed as imprecise. therefore, the theory of fuzzy sets plays an important role in formulating such situations. in this paper, we rst recall the crisp uniformly minimum ...

This paper proposes a novel robust adaptive fuzzy controller for uncertain nonlinear systems with external disturbance. At first, the definitions of compressor and limiter with adjustable parameters are described, an adaptive fuzzy controller with updated laws of the parameters in compressor and estimate values of the fuzzy approximation accuracies is introduced. It is do not need special atten...

Using a set of confidence intervals, we develop a common approach, to construct a fuzzy set as an estimator for unknown parameters in statistical models. We investigate a method to derive the explicit and unique membership function of such fuzzy estimators. The proposed method has been used to derive the fuzzy estimators of the parameters of a Normal distribution and some functions of parameter...